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Author: search.filters.author.Songsuwankit, KanoknuchSubject: search.filters.subject.FFT feature

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    Item type:Publication,
    Low-Cost System for Investigating a Small Motor Fault Classification Based on Current Signal
    (2025-01-01)
    Taweewat, Pat
    ;
    Suwan-Ngam, Warachart
    ;
    Songsuwankit, Kanoknuch
    ;
    Konghuayrob, Poom
    This research presents low-cost system for motor fault classification. This system uses a microcontroller with built-in ADC and communication capability. The two purposes of this article are to investigate the quality of the system for data acquisition and capability of the system for detecting early motor faults both by microcontroller on the system and personal computer. The embedded software on the system is designed to record current signals from a current sensor, compute FFT-based features and classify the fault based on tinyML method. Data communication between the system and the personal computer can be done by both serial port and TCP socket over Wi-Fi. The performances of the system and the personal computer are compared by the experiment as well as the quality of data recorded from the built-in ADC and a digital oscilloscope. The broken rotor bar and bearing fault in a 2.2kW induction motor are investigated. The classifier used in the experiment is a small feed forward neural network which can be implemented on both the proposed low-cost system and the personal computer. Although the recorded electrical current data by built-in ADC is contaminated with noise, the fault classification on the personal computer yield accuracy up to 90%.
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    Comparison of Reduced-Length FFT-Based Feature for Induction Motor Fault Classification
    (2025-01-01)
    Taweewat, Pat
    ;
    Suwan-Ngam, Warachart
    ;
    Songsuwankit, Kanoknuch
    ;
    Konghuayrob, Poom
    This research presents a comparison of FFT-based features which can be used for classifying induction motor faults via neural network. In this paper, the misalignment and rotor bar damage faults are investigated by using stator current as input data only. As the length of the full FFT can include both informative data corresponding to the faults and uninformative data such as noise from environment or electrical supply, only relevant magnitude from FFT bins should be selected and used instead. This paper proposed to use threshold level determined from the magnitude of FFT bins in dataset as a criterion for the selection. From experimental results, an input feature vector created by proposed method can create short input feature vector length to be used by neural network efficiently. The trained neural network performs classification task at 99.98% in accuracy. Comparing to using dimension reduction by PCA, thresholding method needs basic computation, and yields result close to PCA method.